Electronic auscultation devices are gaining increasing popularity in medical industry. Some examples for them combine a stethoscope with an LCD screen and control buttons which can also record and communicate sounds to other devices for later evaluation by health professionals. Audio frequency responses of such devices are similar with acoustic stethoscopes. Their main advantage in comparison with acoustic stethoscopes is amplification ability for easier hearing of said sounds.
Intelligent stethoscopes are not yet common, but considering a few patent applications on this subject, it is possible to expect such devices to be available in medical market.
US 2008 146 276 A1 discloses a device functioning in combination with a telephone, which compares digitized auscultation data with standardized disease sounds on a data storage unit integrated with said telephone, and a disease name appears on the telephone screen. As yet, need for data storage units with sufficient capacities for storing great amounts of data, and requirement of powerful processors for performing sophisticated calculations and algorithms for probabilistic diagnosis limits the use of said idea with every smartphone. Furthermore, the device disclosed in said application transmits only disease names to health professionals, which limits the health professional from using his/her own technical knowledge for reaching a decision by evaluations based on auscultation data.
International patent application WO 2011 049 293 discloses a device combining a stethoscope head with a mobile telephone. The device acquires auscultation sound via a microphone head, and a disease name appears on the telephone screen. Said device employs a temperature sensor which can serve for thermal checking whether the device is in proper contact with a subject's skin, yet such sensor which only aims to detect temperature is not suitable to distinguish whether said device is in contact with the proper body part or zone on a subject's skin to obtain meaningful auscultation data.
It is of vital importance to check whether auscultation data are collected from a proper zone on a subject's skin, and if the sound acquisition device is placed properly such that said meaningful auscultation data are not overridden by ambient noise. Furthermore, auscultation sounds have zone-specific characteristics both in healthy and pathological conditions, which fact necessitates the obligation to evaluate each collected sound data with regard to a corresponding database for each auscultation zone. In prior art, although auscultation sounds are collected with regard to zones of auscultation, auscultation sounds are compared with a single database without regard to distinctions between zones.
One of the most important factors in diagnostic classification problems in terms of reliability is having a sufficiently large database in conjunction with employing a sufficiently powerful processor for rapidly obtaining a general and acceptable result from a sophisticated algorithm. When processing of acquired auscultation data to propose a probabilistic diagnosis is made on a telephone device, the reliability of its results is restricted due to limitations on database size and algorithm sophistication since a phone processor has limited storing and processing capability.
A further important factor is taking into account the information about disease-specific auscultation sounds (e.g. crackles and wheezes) because the presence and characteristics of adventitious sounds are very important in determining the presence, type and severity of an underlying pathology. Although the systems and methods of prior art detect adventitious sounds such as crackles and wheezes, utilization of adventitious sounds in various phases of a respiration cycle in an algorithm which provides a probabilistic suggestion about medical state of a subject is not available in prior art.
U.S. Pat. No. 6,648,820 B1 discloses a medical condition sensing system including a multiplicity of computers connected via a network, yet the vision related to said document is limited to acquisition and comparison of variable data obtained from a single patient at different instances. Said system and related method leads to designate initial data from patient as a baseline, and generates alerts in case of significant variations from the initial data. Evolution of initially present databases related to various health conditions, or creating new databases related to diseases which are initially absent in database collections are unachievable and underivable from such system.
US 2013/0102908 A1 discloses an air conduction sensor and a system and a method for monitoring health condition, U.S. Pat. No. 5,218,969 A discloses an intelligent stethoscope for automatically diagnosing abnormalities; and US 2014/0107515 A1 discloses a telemedical stethoscope which automatically diagnoses diseases, and records and stores data. None of these documents mention nor lead to evolution of initially present databases related to various health conditions, or creating new databases related to diseases which are initially absent in database collections.
U.S. Pat. No. 5,218,969 discloses an intelligent stethoscope for automatically diagnosing abnormalities based on body sounds. Yet, said stethoscope lacks learning abilities since it can't enable the databases loaded thereon to evolve and nor create new databases related to diseases which are initially absent in its database collections. Furthermore, even though said document describes a method to obtain some diagnosis related to heart sounds; the chaotic nature of lung sounds renders said stethoscope inadequate for thorough and multidimensional handling of sound data for suggesting a probabilistic diagnosis.
Scientific research articles are available about computational analyses of auscultation sounds. Said articles generally discuss about acquisition of auscultation data, analog preprocessing i.e. filtration and amplification of said data, analog-to-digital conversion and transferring thereof to computers. Then, said data is subjected to analyses of spectral and temporal signal characteristics, detection and/or classification in terms of adventitious sounds (e.g. crackle and wheeze detection and/or classification), and classification in terms of conditions (e.g. healthy and pathological classification). Yet none of the classification methodologies in the literature uses a sophisticated combination of (i) information about adventitious sounds, (ii) information about general spectral, temporal and spatio-temporal sound characteristics, (iii) distinct information of respiration phases in a full respiration cycle (e.g. early, mid and late phases for both inspiration and expiration in a full respiration cycle), to propose a probabilistic diagnosis about medical state of a subject.